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Predicting patient engagement in IAPT services: a statistical analysis of electronic health records, 2019, Davis et al

Discussion in 'Psychosomatic research - ME/CFS and Long Covid' started by Hutan, Nov 8, 2020.

  1. Hutan

    Hutan Moderator Staff Member

    Aotearoa New Zealand
    Alice Davis, Theresa Smith, Jenny Talbot, Chris Eldridge, David Betts

    Across England, 12% of all improving access to psychological therapy (IAPT) appointments
    are missed, and on average around 40% of first appointments are not attended, varying significantly around the country. In order to intervene effectively, it is important to target the patients who are most likely to miss their appointments.

    This research aims to develop and test a model to predict whether an IAPT patient will attend their first appointment.

    Data from 19 adult IAPT services were analysed in this research. A multiple logistic regression was used at an individual service level to identify
    which patient, appointment and referral characteristics are associated with attendance. These variables were then used in a generalised linear mixed effects model (GLMM). We allow random effects in the GLMM for variables where we observe high service to service heterogeneity in the estimated effects from service specific logistic regressions.

    We find that patients who self-refer are more likely to attend their appointments with an OR of 1.04. The older a patient is, the fewer the number of previous referrals and consenting to receiving a reminder short message service are also found to increase the likelihood of attendance with ORs of 1.02, 1.10, 1.04, respectively.

    Our model is expected to help IAPT services identify which patients are not likely to attend their appointments by highlighting key characteristics that affect attendance.

    Clinical implications
    This analysis will help to identify methods IAPT services could use to increase their attendance rates.


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